Increasing the semantic storage density of sparse distributed memory / Vdovychenko, / Tulchinsky. (2022)
Ukrainian

English  Cybernetics and Systems Analysis   /     Issue (2022, 58 (3))

Vdovychenko R., Tulchinsky V.
Increasing the semantic storage density of sparse distributed memory

The integration of the compressive sensing (CS) method in the sparse distributed memory (SDM) implementation is proposed for increasing the storage capacity for binary sparse distributed representations of semantics, particularly, in graphics processing units (GPUs). © 2022, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: associative memory, binary sparse distributed representations, compressive sensing (CS), GPU, neural networks, sparse distributed memory (SDM), Associative processing, Compressed sensing, Computer graphics, Memory architecture, Program processors, Semantics, Associative memory, Binary sparse distributed representation, Compressive sensing, Distributed representation, Neural-networks, Semantic storages, Sparse distributed memory, Storage densities, Graphics processing unit


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Cite:
Vdovychenko R., Tulchinsky V. (2022). Increasing the semantic storage density of sparse distributed memory. Cybernetics and Systems Analysis, 58 (3), 17–29. doi: https://doi.org/10.1007/s10559-022-00465-y http://jnas.nbuv.gov.ua/article/UJRN-0001323853 [In Ukrainian].


 

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